That made sense when raw model capability was the biggest constraint. Every new frontier model expanded what was possible. But as AI becomes more widely available in different sizes, capabilities and forms, I think the more important question is becoming: how can I leverage all of the intelligence available to me in the best way?

Answering that requires systems flexible enough to take advantage of the leading frontiers of general intelligence while also exceeding those frontiers in areas of deep specialization and knowledge.

Today, we are launching Thomson, our own AI model built specifically for professional work. It is one of the clearest expressions yet of how 抖阴成年 is evolving as an AI technology company, and of a broader change I believe is coming to enterprise AI.

The future will not be every company sending every problem to the largest model available or the model with the best scores across the widest range of benchmarks. It will be organizations developing the ability to apply the right intelligence to the right work.

And as AI becomes core infrastructure for the enterprise, I don鈥檛 think companies will want to outsource every layer of intelligence that determines how their most important work gets done.

Intelligence should fit the task

Ask a rocket scientist to fix an electrical fault in your house.

They could probably work it out, but they鈥檇 likely bring more complexity to the job than it needs. And they may miss things an experienced electrician would catch instinctively. That isn鈥檛 because the electrician鈥檚 job is smaller. It鈥檚 a different job, mastered just as deeply.

AI can work the same way.

Frontier models are extraordinary systems, and they will remain a critical part of the AI stack. There are problems where frontier intelligence is absolutely necessary, and in some cases clearly the best choice. When the path to the right answer is unclear, frontier models excel at figuring it out, drawing on a wide array of tools and information along the way.

But professional work does not always fit that shape.

Professional work is different. A legal brief, a contract, a tax return: these all have outcomes that require a high degree of accuracy, and the people relying on them need to defend and be accountable for them. The defining challenge isn鈥檛 creativity; it鈥檚 precision, and it repeats across thousands of narrow tasks rather than one open-ended one.

All of these tasks do not necessarily need the same model.

That is why model routing and orchestration matter. The system should be able to understand the work being done and determine what kind of intelligence is best suited to it.

For the user, that complexity should largely disappear. They should simply get the best possible outcome, applying their own judgement, experience and oversight where needed.

Thomson is proof that specialized intelligence works. It鈥檚 built to excel at what professional work actually demands: precision, domain fluency and verifiability, optimized specifically for the professional environments we understand best.

Sovereignty is about owning what makes you different

There is another important shift happening alongside this.

For enterprises, AI sovereignty should not mean cutting yourself off from frontier labs or trying to build everything yourself.

AI sovereignty is about owning the layers of the stack that matter to you, but ownership does not mean exclusivity. You can control your own capabilities while still leveraging the frontier of general intelligence for the things it does best. The goal is to own the capabilities that are strategically important differentiators to your business, the things only you can do or that you do better than anyone else.

As access to frontier models becomes broadly available, access itself becomes less differentiating. What matters is what you can build on top of that intelligence and what you can develop that your competitors can鈥檛 simply buy from the same provider.

Companies spend decades building proprietary knowledge, data, expertise and workflows. As AI becomes a more fundamental part of how work gets done, it makes sense that some of that differentiation should exist at the model layer too.

That is part of what Thomson represents for us.

抖阴成年 has deep expertise in professional workflows and authoritative content built over generations. Thomson gives us the ability to encode more of those advantages directly into the intelligence layer itself.

At the same time, we will continue to work with leading frontier model providers. These are complementary capabilities, not competing philosophies.

The opportunity is to know when frontier intelligence is best, when specialized intelligence is best, and how to bring the two together.

A different kind of AI advantage

I think that will become an increasingly important source of competitive advantage.

The advantage won鈥檛 come simply from having access to the most powerful model or from owning your own. It will come from building an AI architecture that can deliberately use different kinds of intelligence based on the work being done.

The first phase of generative AI was largely about proving how capable general-purpose models could become. The next phase will be about engineering those capabilities into systems designed for specific environments, standards and outcomes.

For professional work, the winners will be the organizations that know which intelligence to use, when to use it, and which parts they need to control themselves.

The next competitive advantage in AI will not come from access to intelligence alone. It will come from knowing how to orchestrate it, and knowing which intelligence is important enough to own.

Two Industry Leaders, One Shared Mission

抖阴成年 recently announced that more than 120,000 law students across the U.S. will have access to their latest AI technology, CoCounsel Legal and Deep Research from Westlaw 鈥� making it the first company to provide this scale of 聽fiduciary-grade AI access to law students nationwide. 抖阴成年 serves more than 200 law schools across the U.S., including top-tier T14 institutions, extending a 150-year legacy of supporting legal education.

Hotshot has built a reputation as the go-to learning platform for lawyers at every stage of their careers. Hotshot now helps over half the Am Law 100, top law schools, and in-house teams provide on-demand learning, interactive learning, and experiential training programs in remote, live, and hybrid settings. Founded on the insight that even the smartest people who went to the best law schools don鈥檛 yet know what they’re doing when they start practicing law, Hotshot’s solution was to bring innovations in digital learning to the legal industry 鈥� things like short, practical videos, experiential, hands-on workshops, and mobile learning.

Together, these two organizations are combining the most advanced AI-enabled legal technology in the market with the most practical, skills-based legal training content available 鈥� and putting it directly in the hands of law students before they ever set foot in a law firm.

What the Partnership Delivers

Hotshot and CoCounsel Legal provide law students with hands-on experience to the very latest in AI technology, making them more appealing to employers and preparing them for practice right after graduation. By combining these resources, students gain exposure to cutting-edge AI tools, practical skills and essential methods to succeed from the start of their legal careers. With Hotshot videos, students learn substantive concepts, then use CoCounsel Legal to apply this knowledge 鈥� creating well-structured, research-driven work product that meets future workplace standards.

To give students a clear and structured path to practice readiness, 抖阴成年 and Hotshot have together curated four 鈥減repare to practice鈥� learning tracks built around Hotshot courses: AI Foundations, Professionalism & Business Skills, Civil Litigation Basics, and Transactional Practice Overview. Available to the more than 120,000 law students across the country who already have access to 抖阴成年’ platform, each track brings together short, practical courses designed to deliver immediately applicable knowledge 鈥� and for every course a student completes, they earn a co-branded 抖阴成年 and Hotshot Certificate of Completion that can be added directly to their LinkedIn profile.

These credentials signal to employers that a candidate has already developed the core skills and knowledge the profession demands. That foundational competence, paired with hands-on fluency in CoCounsel Legal, positions students to contribute meaningfully from day one 鈥� and to stand apart in an increasingly competitive market for legal talent.

The Future of Legal Education Starts Now

The legal industry is no longer asking whether AI will transform practice 鈥� it already has. “Professionals are not deciding whether to use AI anymore. They are deciding which AI they trust when their reputation and their clients’ data are on the line,” said Pat Eveland, general manager, Government,聽抖阴成年.

This partnership ensures that when today’s law students make that decision, they’ll do so with the experience, confidence, and skills to use these tools responsibly and effectively. The gap between law school and practice has long been one of the profession’s most persistent challenges. 抖阴成年 and Hotshot are closing it.

鈥淲e couldn鈥檛 be more excited to partner with 抖阴成年 in this way to help so many law students. By combining market-leading technology and foundational knowledge and skills, we鈥檙e helping make sure that students are as prepared for possible to shine in interviews, summer roles, and beyond,鈥� said Ian Nelson, Co-Founder, Hotshot.

Nearly half (47%) of law firm respondents from the Future of Professionals Report say their firms are already experiencing at least one type of benefit from AI adoption and 80% expect AI to fundamentally alter the course of their business over the next five years. Chief among those is time savings. On average, law firm professionals expect to free up 190 hours per year by using AI. At current average hourly rates, that works out to approximately $18,000 in savings per professional, per year 鈥� or a total of $20 billion for the U.S. legal industry.

Perception vs. Reality

For all the enthusiasm that exists for AI鈥檚 potential, however, there is a large gap emerging between law firms鈥� AI aspirations and their real-world AI strategies. Even though the majority of law firms expect AI to drive transformational change in the future and nearly half are experiencing some benefits now, far fewer (29%) expect to see high or transformational levels of change this year. When pressed further on what their firms are doing today to leverage AI, nearly one-third (32%) of law firm respondents say they feel their firms are moving too slowly on AI adoption, and just 22% say their firms have a visible AI strategy in place.

This gap between future ideals and current realities is a phenomenon 鈥渢he GenAI paradox,鈥� which occurs when businesses race to invest in AI pilot projects and buy new solutions, but struggle when it comes to implementing them and integrating them into everyday workflows. Versions of this struggle are playing out in virtually every industry right now as professionals come to grips with the fact that true transformation is not as simple as plugging in an off-the-shelf AI tool. It requires a clear strategy, a carefully planned roadmap, targeted integration of professional-grade AI solutions, and a commitment at all levels for the long haul. A firm cannot afford to sit on the sidelines any longer 鈥� it is imperative to have an AI strategy.

Key Steps to True AI Transformation

Over the course of our partnerships helping some of the world鈥檚 largest law firms not only access new AI capabilities, but , we鈥檝e found four levers that all firms need to engage to ensure the success of their AI initiatives.

• AI Tools Without an AI Strategy will Never Reach Their Potential: Among the 22% of law firms that currently have a visible AI strategy in place, 71% are already experiencing a clear return on investment from AI. By contrast, for those firms that do not have a clear AI strategy in place, just 18% are experiencing a return on investment. That means law firms with a visible AI strategy are almost four times more likely to experience benefits compared to firms without any significant plans for AI adoption.
• AI Leadership Comes from the Top: Law firms helmed by leaders who lead by example when introducing change, firms that have added new governance roles, and those that are actively investing in AI are consistently seeing more benefits than those that don鈥檛. For AI to truly add value, it needs to be implemented firm-wide, and that kind of sweeping change can only come with leadership support, clear goals and objectives, and widespread adoption.
• Operations is Where the Hard Work Happens: Firm-wide AI integration is impossible without first understanding the need to change and reimagining workflows. To extract maximum value, AI-powered tools must be built directly into existing systems and processes. That requires making transformative changes to underlying business models, including how firms price, staff, and deliver legal work, and how they adapt related workflows and processes, while adding new roles and skills to support their operations.
• End-User Adoption: The best AI technology and most well-thought-out strategy in the world will not mean anything if no one uses it. When users within law firms understand AI and feel empowerment, ownership, and accountability for its use, their law firms see results not only in terms of higher levels of AI adoption but in the additional benefits and ROI that they gain as well. Firms need to make sure they are educating staff, making tools readily available, and allowing time for a learning curve to take root.

While the detailed strategies and specific paths to AI implementation will vary from firm to firm, there are a handful of universal truths that apply to all. Foremost is the commitment to address the AI revolution for what it really is 鈥� a monumental transformation in the way legal work is conducted on par with the introduction of the personal computer, the internet, and the smartphone. It is not enough just to buy the latest greatest widget. Firms that want to extract real value from AI need to think hard about how it will affect everything they do and start addressing those changes now to unlock the full potential of the technology to transform their firm.